Large generative models may be a necessary basis for broad swaths of novel interdisciplinary AI research on topics ranging from linguistics and robotics to philosophy and the social sciences [1]}. Without the development of (or at least access to) large models, it will be challenging to research how they may advance progress in societally impactful research domains such as healthcare, education, and law [1]}. Large models are also fertile testing grounds for developing next-generation algorithms and architectures — novel algorithms can be rigorously evaluated according to whether they advantageously shift scaling laws to be more compute, data, or parameter efficient.
